estimating cancer survival in small areas: possible and...
TRANSCRIPT
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Estimating cancer survival in small areas: possible and useful
Susanna Cramb, Kerrie Mengersen and Peter Baade
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Survival
• The proportion who survive a given length of time after diagnosis
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Survival
• Key measure of cancer patient care
• Allows monitoring and evaluation of health services
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Estimating Net Survival
Cause-specific Relative
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Estimating Net Survival
Cause-specific Relative
Based on death certificate
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Estimating Net Survival
Cause-specific Relative
Based on death certificate
Compares against population mortality
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Estimating Net Survival
Cause-specific Relative
Based on death certificate
Compares against population mortality
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Data sources
• Cancer incidence data (contains death information)
Queensland Cancer Registry (population-based)
• Unit record file mortality data by age group, sex, time and area
Australian Bureau of Statistics
• Population data by age group, sex, time and area
Australian Bureau of Statistics
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Data preparation 1. Population mortality data
• Create lifetables by SLA, sex and year group (e.g. 2003-2007).
2. Cancer incidence data
• Calculate the person-time at risk, and the expected deaths using the lifetable data.
3. Neighbourhood adjacency matrix file
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Data preparation 1. Population mortality data
• Create lifetables by SLA, sex and year group (e.g. 2003-2007).
2. Cancer incidence data
• Calculate the person-time at risk, and the expected deaths using the lifetable data.
3. Neighbourhood adjacency matrix file
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Data preparation 1. Population mortality data
• Create lifetables by SLA, sex and year group (e.g. 2003-2007).
2. Cancer incidence data
• Calculate the person-time at risk, and the expected deaths using the lifetable data.
3. Neighbourhood adjacency matrix file
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Relative survival model
Dickman et al. (2004):
dj ~ Poisson(μj)
log(μj – d*j) = log(yj) + xβ
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Relative survival model
Dickman et al. (2004):
dj ~ Poisson(μj)
log(μj – d*j) = log(yj) + xβ
Excess deaths
Person-time at risk
}
Covariate parameters
Observed deaths
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Bayesian relative survival model
Based on Fairley et al (2008):
dkji ~ Poisson(μkji)
log(μkji – d*kji) = log(ykji)+ αj + xβk + ui + vi
where k = broad age groups
j = 1,2,…5 follow-up years
i = 1,2,…478 SLAs
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Bayesian relative survival model
Based on Fairley et al (2008):
dkji ~ Poisson(μkji)
log(μkji – d*kji) = log(ykji)+ αj + xβk + ui + vi
where k = broad age groups
j = 1,2,…5 follow-up years
i = 1,2,…478 SLAs
Intercept Unobserved and unstructured
Unobserved with spatial structure
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The Bayesian difference
• Parameters considered to arise from underlying distribution (“stochastic”) • Use probability distributions (“priors”)
• Simplifies inclusion of spatial relationships
• Posterior distributions for output parameters
• Posterior proportional to Likelihood x Prior
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Posterior distributions Trace plot Density plot
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Bayesian relative survival model
Based on Fairley et al (2008):
dkji ~ Poisson(μkji)
log(μkji – d*kji) = log(ykji)+ αj + xβk + ui + vi
where k = broad age groups
j = 1,2,…5 follow-up years
i = 1,2,…478 SLAs
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Bayesian relative survival model
Based on Fairley et al (2008):
dkji ~ Poisson(μkji)
log(μkji – d*kji) = log(ykji)+ αj + xβk + ui + vi
where k = broad age groups
j = 1,2,…5 follow-up years
i = 1,2,…478 SLAs
e.g. ~Normal(0,1000)
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Bayesian relative survival model
Based on Fairley et al (2008):
dkji ~ Poisson(μkji)
log(μkji – d*kji) = log(ykji)+ αj + xβk + ui + vi
where k = broad age groups
j = 1,2,…5 follow-up years
i = 1,2,…478 SLAs
e.g. ~Normal(0,1000)
CAR prior
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The Conditional AutoRegressive (CAR) distribution
Area full conditional distributions:
𝑝 𝑢𝑖 𝑢𝑗 , 𝑖 ≠ 𝑗, 𝜎2 ~𝑁 𝜇 𝑖 ,
𝜎2
𝑛𝛿𝑖
𝜇 𝑖 = 𝑢𝑗
𝑛𝛿𝑖𝑗∈𝛿𝑖
𝑛𝛿𝑖 = number of neighbours
𝜎2 = variance
uj uj
uj uj uj
uj
uj ui
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Raw estimates RER
Breast cancer survival (risk of death within 5 years)
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Raw estimates RER
Problems
• Many large areas have small populations (and vice versa)
• Excessive random variation – obscures the true geographic pattern
Breast cancer survival (risk of death within 5 years)
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Raw estimates Smoothed estimates RER
Breast cancer survival (risk of death within 5 years)
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Results and Benefits
This model allows us to determine: • Robust small area estimates with uncertainty
• Influence of important covariates
• Probabilities (e.g. probability RER > 1)
• Ranking
• Number of deaths resulting from spatial inequalities
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Graphs
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Bayesian relative survival model
Breast and colorectal cancers
dkji ~ Poisson(μkji)
log(μkji – d*kji) = log(ykji)+ αj + xβk + vi + ui
where k = broad age groups/SES/remoteness/stage/gender
j = 1,2,…5 follow-up years
i = 1,2,…478 SLAs
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Bayesian relative survival model
Breast and colorectal cancers
dkji ~ Poisson(μkji)
log(μkji – d*kji) = log(ykji)+ αj + xβk + vi + ui
where k = broad age groups/SES/remoteness/stage/gender
j = 1,2,…5 follow-up years
i = 1,2,…478 SLAs
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Breast cancer survival (risk of death within 5 years) Adjusted for age Adjusted for age & stage
RER Spatial variation p-value=0.001 Spatial variation p-value=0.042
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Breast cancer survival (risk of death within 5 years) Adjusted for age, stage & SES Adjusted for age , stage, SES & distance
RER Spatial variation p-value=0.452 Spatial variation p-value=0.631
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How many deaths could be prevented if no spatial inequalities?
Number of deaths within 5 years from diagnosis due to non-diagnostic spatial inequalities (1997-2008):
Colorectal cancer: Breast cancer:
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How many deaths could be prevented if no spatial inequalities?
Number of deaths within 5 years from diagnosis due to non-diagnostic spatial inequalities (1997-2008):
Colorectal cancer: Breast cancer:
470 (7.8%) 170 (7.1%)
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• Neighbourhood matrix created in GeoDa (https://geodacenter.asu.edu/)
• Ran in WinBUGS (Bayesian inference Using Gibbs Sampling) interfaced with Stata
• Freely available at: www.mrc-bsu.cam.ac.uk/bugs
• 250,000 iterations discarded, 100,000 iterations monitored (kept every 10th)
• Time taken: 3 hours 15 minutes+
• On a dedicated server:
• Dual CPU Quad Core Xeon E5520’s: 8 Cores and 16 Threads, large 8MB Cache
• Quick Path Interconnect: fast memory access
Implementation
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Cramb SM, Mengersen KL, Baade PD. 2011. The Atlas of Cancer in Queensland: Geographical
variation in incidence and survival, 1998-2007. Cancer Council Queensland: Brisbane.
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Cramb SM, Mengersen KL, Baade PD. 2011. The Atlas of Cancer in Queensland: Geographical
variation in incidence and survival, 1998-2007. Cancer Council Queensland: Brisbane.
Cramb SM, Mengersen KL, Baade PD. 2011. Developing the atlas of cancer in Queensland:
methodological issues. Int J Health Geogr, 10:9
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Cramb SM, Mengersen KL, Baade PD. 2011. The Atlas of Cancer in Queensland: Geographical
variation in incidence and survival, 1998-2007. Cancer Council Queensland: Brisbane.
Cramb SM, Mengersen KL, Baade PD. 2011. Developing the atlas of cancer in Queensland:
methodological issues. Int J Health Geogr, 10:9
Cramb SM, Mengersen KL, Turrell G, Baade PD. 2012. Spatial inequalities in colorectal and breast
cancer survival: Premature deaths and associated factors. Health & Place;18:1412-21.
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Cramb SM, Mengersen KL, Baade PD. 2011. The Atlas of Cancer in Queensland: Geographical
variation in incidence and survival, 1998-2007. Cancer Council Queensland: Brisbane.
Cramb SM, Mengersen KL, Baade PD. 2011. Developing the atlas of cancer in Queensland:
methodological issues. Int J Health Geogr, 10:9
Cramb SM, Mengersen KL, Turrell G, Baade PD. 2012. Spatial inequalities in colorectal and breast
cancer survival: Premature deaths and associated factors. Health & Place;18:1412-21.
Earnest A, Cramb SM, White NM. 2013. Disease mapping using Bayesian hierarchical models. In
Alston CL, Mengersen KL, Pettitt AN (eds): Case Studies in Bayesian Statistical Modelling and
Analysis, Wiley: Chichester.
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“By increasing our understanding of the small
area inequalities in cancer outcomes, this
type of innovative modelling provides us with
a better platform to influence government
policy, monitor changes, and allocate Cancer
Council Queensland resources”
~ Professor Jeff Dunn, Cancer Council Queensland CEO